• Class and Course

    Applied Statistical Modeling and Data Analytics

    This course discusses:

    •Visualizing univariate, bivariate and multivariate data
    •Fitting simple and multiple linear regression models to observed data
    •Developing a non-parametric regression model from given data
    •Reducing data dimensionality with Principal Component Analysis
    •Grouping data with k-means and hierarchical clustering
    •Identifying classification boundary between clusters using discriminant analysis
    •Applying machine learning techniques (e.g., random forest, gradient boosting machine, support vector regression, kriging model, neural networks) for predictive modeling
    •Generating decision rules with classification tree analysis
    •Translating model input uncertainty into uncertainty in model predictions using Monte Carlo simulation and analytical alternatives
    •Analyzing input-output dependencies from Monte-Carlo simulation results
    •Creating an experimental design and fitting a response surface to the results
    •Hybrid modeling combining data-driven and physics-based models

    •Introduction  - Overview and An Illustrative Example
    •Statistical Background and Exploratory Data Analysis
    •Regression Modeling and Applications
    •Computer Exercise: HMQI
    •Modeling Spatial Variation
    •Spatial Modeling/interpolation of Properties
    •Computer Exercises: SGEMS

    •Optimal Transformation for Multiple Regression
    •Non-Parametric Regression
    •Computer Exercises: GRACE
    •Variable Selection: Stepwise Regression
    •Principal Component Analysis
    •Cluster and Discriminant Analysis
    •Exercises: EFACIES

    •Classification and Regression Trees
    •Advanced Machine Learning Methods: RF, GBM, SVM, NNET
    •Computer Exercises: RATTLE
    •Application: Rate Decline in Unconventional Reservoirs
    •Application: Hybrid Reservoir Modeling
    •Application: Surrogate Modeling
    •Computer Exercise: GRACE/RATTLE

    •Experimental Design and Response Surface Analysis
    •History Matching Using Response Surface and Genetic Algorithm
    •Computer Exercises: EREGRESS/ GLOBAL
    •Data-Physics Modeling: Rate and Pressure Data Analysis
    •Data-Physics Modeling: Capacitance Resistance Models
    •Computer Exercises: CRM / SPADES

    •Uncertainty Quantification: Background
    •Uncertainty Characterization
    •Uncertainty Propagation
    •Uncertainty Importance Analysis
    •Wrap-up and Key Takeaways
    •Workshop Conclusions

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